Loop closure

ABSTRACT

Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. In particular, a multi-view interactive digital media representation can be generated from live images captured from a camera. The live images can include an object. An angular view of the object captured in the live images can be estimated using sensor data from an inertial measurement unit. The determined angular views can be used to select from among the live images and determine when a three hundred sixty degree view of the object has been captured. When the plurality of images is output to a display, the object can appear to undergo a 3-D rotation, such as a three hundred sixty degree rotation, through the determined angular view where the 3-D rotation of the object is generated without a 3-D polygon model of the object.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent document is a continuation of and claims priority to U.S.patent appl. Ser. No. 15/601,893 (Attorney Docket No. FYSNP033) byHolzer et al., filed on May 22, 2017, entitled, “Loop Closure.” U.S.patent appl. Ser. No. 15/601,893 is hereby incorporated by reference inits entirety and for all purposes.

TECHNICAL FIELD

The present disclosure relates to generating and manipulating multi-viewinteractive digital media representations.

With modern computing platforms and technologies shifting towards mobileand wearable devices that include camera sensors as native acquisitioninput streams, the desire to record and preserve moments digitally in adifferent form than more traditional two-dimensional (2D) flat imagesand videos has become more apparent. Traditional digital media formatstypically limit their viewers to a passive experience. For instance, a2D flat image can be viewed from one angle and is limited to zooming inand out. Accordingly, traditional digital media formats, such as 2D flatimages, do not easily lend themselves to reproducing memories and eventswith high fidelity.

Current predictions (Ref: KPCB “Internet Trends 2012” presentation”)indicate that every several years the quantity of visual data that isbeing captured digitally online will double. As this quantity of visualdata increases, so does the need for much more comprehensive search andindexing mechanisms than ones currently available. Unfortunately,neither 2D images nor 2D videos have been designed for these purposes.Accordingly, improved mechanisms that allow users to view and indexvisual data, as well as query and quickly receive meaningful resultsfrom visual data are desirable.

OVERVIEW

Various embodiments of the present invention relate generally to systemsand methods for analyzing and manipulating images and video. Accordingto particular embodiments, the spatial relationship between multipleimages and video is analyzed together with location information data,for purposes of creating a representation referred to herein as amulti-view interactive digital media representations. The multi-viewinteractive digital media representations can be output to a device witha display, such as a mobile device, tablet computer or laptop computer.

Multi-view interactive digital media representations can include imagesof an object from many different viewing angles. Images with viewingangles about a common axis can be grouped together. These images can beprovided in a sequence where the viewing angle changes from image toimage in the sequence in an orderly manner. Thus, as the sequence ofimages is viewed on a display, the object can appear to rotate about thecommon axis. In particular embodiments, a multi-view interactive digitalmedia representation can be provided with images with viewing anglesabout one or more axes. Thus, when viewed the object in the multi-viewinteractive digital media representation can appear to rotate about theone or more axes.

In more detail, a multi-view interactive digital media representationcan be generated from live images captured from a camera. The liveimages can include an object. An angular view of the object captured inthe live images can be estimated using sensor data from an inertialmeasurement unit such that an angular view can be associated with eachof the live image. The angular view associated with each live image canbe used select live images. For example, live images separated by anangular view amount, such as one, two, three, five, ten degrees, etc.,can be selected.

The multi-view interactive digital media representation can include aplurality of images where each of the plurality of images includes theobject from a different camera view. For example, the live imagesseparated by an angular view amount can be selected. When the pluralityof images is output to a display, the object can appear to undergo a 3-Drotation through the determined angular view where the 3-D rotation ofthe object is generated without a 3-D polygon model of the object.

One method can be generally characterized as comprising, on a mobiledevice including a processor, a memory, a camera, an inertialmeasurement unit, a microphone, a GPS sensor and a touchscreen display,receiving a request to generate a multi-view interactive digital mediarepresentation of an object. Then, a sequence live images can bereceived from the camera on the mobile device where the live images caninclude 2-D pixel data. The camera can move along a path where anorientation of the camera varies along the path such that the object inthe sequence of the live images is captured from a plurality of cameraviews.

Based upon sensor data from the inertial measurement unit, angularchanges in the orientation of the camera along the path can bedetermined. Based upon the angular changes, an angular view of theobject captured in the sequence of the live images can be determined.From the sequence of the live images, the multi-view interactive digitalmedia representation can be generated.

In one embodiment, a three hundred sixty degree angular view of theobject can be captured. The estimated angular views associated with eachlive image can be used to determine when a three hundred sixty degreeangular view of the object has been reached. Further, the estimatedangular views can be used to select images at pre-defined angles toutilize in the multi-view interactive digital media representation.

The multi-view interactive digital media representation can include aplurality of images where each of the plurality of images can includethe object from a different camera view. When the plurality of images isoutput to the touchscreen display, the object can appear to undergo a3-D rotation through the angular view, such as a three hundred sixtydegree rotation. The 3-D rotation of the object can be generated withouta 3-D polygon model of the object. A value of the angular view of theobject captured in the multi-view interactive digital mediarepresentation can output, such as via the touchscreen display.

In another embodiment, a method can be generated on a mobile device. Themobile device can include a processor, a memory, a camera, an inertialmeasurement unit, a microphone and a touchscreen display. The method canbe generally characterized as 1) receiving, via an input interface, onthe mobile device, a request to generate a multi-view interactivedigital media representation of an object, 2) receiving via the inputinterface an angle profile where the angle profile can include aplurality of angles or angle information used to determine the pluralityof angles, 3) receiving live images from the camera on the mobile deviceas the mobile device moves along a path where an orientation of thecamera varies along the path such that the object in the live images iscaptured from a plurality of camera views; 4) based upon sensor datafrom the inertial measurement unit, determining angular changes in theorientation of the camera along the path; 5) based upon the angularchanges, determining an angular view of the object captured in each ofthe live images; 6) based upon the determined angular view of the objectin each of the live images and the plurality of angles in the angleprofile, selecting a sequence of images from among the live images whereone of the live images is selected for each of the plurality of angles;and 7) generating from the sequence of the images the multi-viewinteractive digital media representation. The multi-view interactivedigital media representation can include a plurality of images whereeach of the plurality of images includes the object from a differentcamera view. When the plurality of images is output to the touchscreendisplay the object appears to undergo a 3-D rotation through the angularview wherein the 3-D rotation of the object is generated without a 3-Dpolygon model of the object.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular embodiments of the present invention.

FIG. 1 illustrates an example of a multi-view interactive digital mediarepresentation acquisition system in accordance with embodiments of thepresent invention.

FIG. 2 illustrates an example of a process flow for generating amulti-view interactive digital media representation in accordance withembodiments of the present invention.

FIG. 3 illustrates one example of multiple camera views that can befused into a three-dimensional (3D) model to create an immersiveexperience in accordance with embodiments of the present invention.

FIG. 4 illustrates one example of separation of content and context in amulti-view interactive digital media representation in accordance withembodiments of the present invention.

FIGS. 5A-5B illustrate examples of concave view and convex views,respectively, where both views use a back-camera capture style inaccordance with embodiments of the present invention.

FIGS. 6A to 6D illustrate examples of various capture modes formulti-view interactive digital media representations in accordance withembodiments of the present invention.

FIG. 7A illustrates a sensor package for determining orientation of acamera used to generate a MVIDMR in accordance with embodiments of thepresent invention.

FIG. 7B illustrates a mobile device and body-centric coordinate systemin accordance with embodiments of the present invention.

FIG. 8A and 8B illustrate roll orientations of a mobile device with acamera as a function of time along a path during MVIDMR generationdepicted in different coordinate systems in accordance with embodimentsof the present invention.

FIG. 8C and 8D illustrates rotations of a mobile device with a cameraabout an axis as a function of time for paths shown in FIG. 8B inaccordance with embodiments of the present invention.

FIG. 9A illustrates pitch and roll of a mobile device and angle changesas a function of time relative to the gravity vector during MVIDMRgeneration in accordance with embodiments of the present invention.

FIG. 9B illustrates pitch and roll of a mobile device and angle changesas a function of time relative to an arbitrary axis during MVIDMRgeneration in accordance with embodiments of the present invention.

FIG. 10A and 10B illustrate pitch and roll of a device with a cameraalong two different paths during image capture associated with a MVIDMRin accordance with embodiments of the present invention.

FIG. 11A and 11B illustrate angle change reporting during image captureassociated with a MVIDMR in accordance with embodiments of the presentinvention.

FIG. 12 illustrates an example of a process flow for generating a MVIDMRusing IMU data in accordance with embodiments of the present invention.

FIG. 13A illustrates a setup for generating an MVIDMR including a threehundred sixty degree of an object in accordance with embodiments of thepresent invention.

FIG. 13B illustrates live images selected for use in an MVIDMR basedupon angle estimation using IMU data in accordance with embodiments ofthe present invention.

FIG. 14 illustrates different angle profiles for selecting live imagesfor using in an MVIDMR in accordance with embodiments of the presentinvention.

FIG. 15 illustrates an example of a process flow for generating a MVIDMRusing IMU data for image selection in accordance with embodiments of thepresent invention.

FIG. 16 illustrates a particular example of a computer system that canbe used with various embodiments of the present invention.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examples of theinvention including the best modes contemplated by the inventors forcarrying out the invention. Examples of these specific embodiments areillustrated in the accompanying drawings. While the present disclosureis described in conjunction with these specific embodiments, it will beunderstood that it is not intended to limit the invention to thedescribed embodiments. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the invention as defined by the appended claims.

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the present invention.Particular embodiments of the present invention may be implementedwithout some or all of these specific details. In other instances, wellknown process operations have not been described in detail in order notto unnecessarily obscure the present invention.

Various aspects of the present invention relate generally to systems andmethods for analyzing the spatial relationship between multiple imagesand video together with location information data, for the purpose ofcreating a single representation, a multi-view interactive digital mediarepresentation (MVIDMR), which eliminates redundancy in the data, andpresents a user with an interactive and immersive active viewingexperience. According to various embodiments, active is described in thecontext of providing a user with the ability to control the viewpoint ofthe visual information displayed on a screen.

Next, with respect to FIGS. 1-16 methods and apparatus for acquiringimage data and generating a multi-view interactive digital mediarepresentations (MVIDMRs) are discussed. In particular, an example ofMVIDMR system is discussed with respect to FIG. 1. An example of aprocess flow for generating an MVIDMR is described. With respect to FIG.3, one example of multiple camera views that can be fused into athree-dimensional (3D) model to create an immersive experience isdiscussed. With respect to FIG. 4, one example of separating content andcontext for MVIDMR generation is described. Examples of concave view andconvex views, respectively, where both views use a back-camera capturestyle are described with respect to FIGS. 5A and 5B. Various capturemodes, which can be used in MVIDMR generation, are discussed withrespect to FIGS. 6A to 6D.

With respect to FIGS. 7A to 15, system and methods for generatingmulti-view interactive digital media representations (MVIDMRs) usingangle measurements derived from sensor data are described. In moredetail, with respect to FIGS. 7A and 7B, sensor packages, theirintegration into a mobile device and sensor outputs are described. Withrespect to FIGS. 8A to 9B, rotation metrics determined from IMU data,which can be generated for a MVIDMR, are described. With respect FIGS.10A, 10B and 11, the application of rotation metrics to MVIDMRgeneration for different image capture paths are discussed. A method ofMVIDMR generation is described with respect to FIG. 12.

With respect to FIG. 13A, a setup for generating an MVIDMR including athree hundred sixty degree of an object is discussed. With respect toFIG. 13B, live images selected for use in an MVIDMR based upon angleestimation using IMU data are described. The live images are generatedusing the setup in FIG. 13A. With respect to FIG. 14, different angleprofiles for selecting live images for using in an MVIDMR are discussed.An example of a process flow for generating a MVIDMR using IMU data forimage selection is described with respect to FIG. 15. Finally, withrespect to FIG. 16, an example of an apparatus, which can be used duringMVIDMR generation, is discussed.

With reference to FIG. 1, shown is one example of a multi-viewinteractive digital media representation acquisition system 100. In thepresent example embodiment, the multi-view interactive digital mediarepresentation acquisition system 100 is depicted in a flow sequencethat can be used to generate a multi-view interactive digital mediarepresentation. According to various embodiments, the data used togenerate a multi-view interactive digital media representation can comefrom a variety of sources.

In particular, data such as, but not limited to two-dimensional (2D)images 104 can be used to generate a multi-view interactive digitalmedia representation. These 2D images can include color image datastreams such as multiple image sequences, video data, etc., or multipleimages in any of various formats for images, depending on theapplication. Another source of data that can be used to generate amulti-view interactive digital media representation includes environmentinformation 106. This environment information 106 can be obtained fromsources such as accelerometers, gyroscopes, magnetometers, GPS, WiFi,IMU-like systems (Inertial Measurement Unit systems), and the like. Somemethods of utilizing the IMU to generate a multi-view interactivedigital media representation are described in more detail below withrespect to FIGS. 7-15. Yet another source of data that can be used togenerate a multi-view interactive digital media representation caninclude depth images 108. These depth images can include depth, 3D, ordisparity image data streams, and the like, and can be captured bydevices such as, but not limited to, stereo cameras, time-of-flightcameras, three-dimensional cameras, and the like.

In the present example embodiment, the data can then be fused togetherat sensor fusion block 110. In some embodiments, a multi-viewinteractive digital media representation can be generated a combinationof data that includes both 2D images 104 and environment information106, without any depth images 108 provided. In other embodiments, depthimages 108 and environment information 106 can be used together atsensor fusion block 110. Various combinations of image data can be usedwith environment information at 106, depending on the application andavailable data.

In the present example embodiment, the data that has been fused togetherat sensor fusion block 110 is then used for content modeling 112 andcontext modeling 114. As described in more detail with regard to FIG. 4,the subject matter featured in the images can be separated into contentand context. The content can be delineated as the object of interest andthe context can be delineated as the scenery surrounding the object ofinterest. According to various embodiments, the content can be athree-dimensional model, depicting an object of interest, although thecontent can be a two-dimensional image in some embodiments, as describedin more detail below with regard to FIG. 4. Furthermore, in someembodiments, the context can be a two-dimensional model depicting thescenery surrounding the object of interest. Although in many examplesthe context can provide two-dimensional views of the scenery surroundingthe object of interest, the context can also include three-dimensionalaspects in some embodiments. For instance, the context can be depictedas a “flat” image along a cylindrical “canvas,” such that the “flat”image appears on the surface of a cylinder. In addition, some examplesmay include three-dimensional context models, such as when some objectsare identified in the surrounding scenery as three-dimensional objects.According to various embodiments, the models provided by contentmodeling 112 and context modeling 114 can be generated by combining theimage and location information data, as described in more detail withregard to FIG. 3.

According to various embodiments, context and content of a multi-viewinteractive digital media representation are determined based on aspecified object of interest. In some examples, an object of interest isautomatically chosen based on processing of the image and locationinformation data. For instance, if a dominant object is detected in aseries of images, this object can be selected as the content. In otherexamples, a user specified target 102 can be chosen, as shown in FIG. 1.It should be noted, however, that a multi-view interactive digital mediarepresentation can be generated without a user specified target in someapplications.

In the present example embodiment, one or more enhancement algorithmscan be applied at enhancement algorithm(s) block 116. In particularexample embodiments, various algorithms can be employed during captureof multi-view interactive digital media representation data, regardlessof the type of capture mode employed. These algorithms can be used toenhance the user experience. For instance, automatic frame selection,stabilization, view interpolation, filters, and/or compression can beused during capture of multi-view interactive digital mediarepresentation data. In some examples, these enhancement algorithms canbe applied to image data after acquisition of the data. In otherexamples, these enhancement algorithms can be applied to image dataduring capture of multi-view interactive digital media representationdata.

According to particular example embodiments, automatic frame selectioncan be used to create a more enjoyable multi-view interactive digitalmedia representation. Specifically, frames are automatically selected sothat the transition between them will be smoother or more even. Thisautomatic frame selection can incorporate blur- andoverexposure-detection in some applications, as well as more uniformlysampling poses such that they are more evenly distributed.

In some example embodiments, stabilization can be used for a multi-viewinteractive digital media representation in a manner similar to thatused for video. In particular, key frames in a multi-view interactivedigital media representation can be stabilized for to produceimprovements such as smoother transitions, improved/enhanced focus onthe content, etc. However, unlike video, there are many additionalsources of stabilization for a multi-view interactive digital mediarepresentation, such as by using IMU information, depth information,computer vision techniques, direct selection of an area to bestabilized, face detection, and the like.

For instance, IMU information can be very helpful for stabilization. Inparticular, IMU information provides an estimate, although sometimes arough or noisy estimate, of the camera tremor that may occur duringimage capture. This estimate can be used to remove, cancel, and/orreduce the effects of such camera tremor.

In some examples, depth information, if available, can be used toprovide stabilization for a multi-view interactive digital mediarepresentation. Because points of interest in a multi-view interactivedigital media representation are three-dimensional, rather thantwo-dimensional, these points of interest are more constrained andtracking/matching of these points is simplified as the search spacereduces. Furthermore, descriptors for points of interest can use bothcolor and depth information and therefore, become more discriminative.In addition, automatic or semi-automatic content selection can be easierto provide with depth information. For instance, when a user selects aparticular pixel of an image, this selection can be expanded to fill theentire surface that touches it. Furthermore, content can also beselected automatically by using a foreground/background differentiationbased on depth. In various examples, the content can stay relativelystable/visible even when the context changes.

According to various examples, computer vision techniques can also beused to provide stabilization for multi-view interactive digital mediarepresentations. For instance, keypoints can be detected and tracked.However, in certain scenes, such as a dynamic scene or static scene withparallax, no simple warp exists that can stabilize everything.Consequently, there is a trade-off in which certain aspects of the scenereceive more attention to stabilization and other aspects of the scenereceive less attention. Because a multi-view interactive digital mediarepresentation is often focused on a particular object of interest, amulti-view interactive digital media representation can becontent-weighted so that the object of interest is maximally stabilizedin some examples.

Another way to improve stabilization in a multi-view interactive digitalmedia representation includes direct selection of a region of a screen.For instance, if a user taps to focus on a region of a screen, thenrecords a convex multi-view interactive digital media representation,the area that was tapped can be maximally stabilized. This allowsstabilization algorithms to be focused on a particular area or object ofinterest.

In some examples, face detection can be used to provide stabilization.For instance, when recording with a front-facing camera, it is oftenlikely that the user is the object of interest in the scene. Thus, facedetection can be used to weight stabilization about that region. Whenface detection is precise enough, facial features themselves (such aseyes, nose, and mouth) can be used as areas to stabilize, rather thanusing generic keypoints. In another example, a user can select an areaof image to use as a source for keypoints.

According to various examples, view interpolation can be used to improvethe viewing experience. In particular, to avoid sudden “jumps” betweenstabilized frames, synthetic, intermediate views can be rendered on thefly. This can be informed by content-weighted keypoint tracks and IMUinformation as described above, as well as by denser pixel-to-pixelmatches. If depth information is available, fewer artifacts resultingfrom mismatched pixels may occur, thereby simplifying the process. Asdescribed above, view interpolation can be applied during capture of amulti-view interactive digital media representation in some embodiments.In other embodiments, view interpolation can be applied duringmulti-view interactive digital media representation generation.

In some examples, filters can also be used during capture or generationof a multi-view interactive digital media representation to enhance theviewing experience. Just as many popular photo sharing services provideaesthetic filters that can be applied to static, two-dimensional images,aesthetic filters can similarly be applied to surround images. However,because a multi-view interactive digital media representation is moreexpressive than a two-dimensional image, and three-dimensionalinformation is available in a multi-view interactive digital mediarepresentation, these filters can be extended to include effects thatare ill-defined in two dimensional photos. For instance, in a multi-viewinteractive digital media representation, motion blur can be added tothe background (i.e. context) while the content remains crisp. Inanother example, a drop-shadow can be added to the object of interest ina multi-view interactive digital media representation.

In various examples, compression can also be used as an enhancementalgorithm 116. In particular, compression can be used to enhanceuser-experience by reducing data upload and download costs. Becausemulti-view interactive digital media representations use spatialinformation, far less data can be sent for a multi-view interactivedigital media representation than a typical video, while maintainingdesired qualities of the multi-view interactive digital mediarepresentation. Specifically, the IMU, keypoint tracks, and user input,combined with the view interpolation described above, can all reduce theamount of data that must be transferred to and from a device duringupload or download of a multi-view interactive digital mediarepresentation. For instance, if an object of interest can be properlyidentified, a variable compression style can be chosen for the contentand context. This variable compression style can include lower qualityresolution for background information (i.e. context) and higher qualityresolution for foreground information (i.e. content) in some examples.In such examples, the amount of data transmitted can be reduced bysacrificing some of the context quality, while maintaining a desiredlevel of quality for the content.

In the present embodiment, a multi-view interactive digital mediarepresentation 118 is generated after any enhancement algorithms areapplied. The multi-view interactive digital media representation canprovide a multi-view interactive digital media representation. Invarious examples, the multi-view interactive digital mediarepresentation can include three-dimensional model of the content and atwo-dimensional model of the context. However, in some examples, thecontext can represent a “flat” view of the scenery or background asprojected along a surface, such as a cylindrical or other-shapedsurface, such that the context is not purely two-dimensional. In yetother examples, the context can include three-dimensional aspects.

According to various embodiments, multi-view interactive digital mediarepresentations provide numerous advantages over traditionaltwo-dimensional images or videos. Some of these advantages include: theability to cope with moving scenery, a moving acquisition device, orboth; the ability to model parts of the scene in three-dimensions; theability to remove unnecessary, redundant information and reduce thememory footprint of the output dataset; the ability to distinguishbetween content and context; the ability to use the distinction betweencontent and context for improvements in the user-experience; the abilityto use the distinction between content and context for improvements inmemory footprint (an example would be high quality compression ofcontent and low quality compression of context); the ability toassociate special feature descriptors with multi-view interactivedigital media representations that allow the multi-view interactivedigital media representations to be indexed with a high degree ofefficiency and accuracy; and the ability of the user to interact andchange the viewpoint of the multi-view interactive digital mediarepresentation. In particular example embodiments, the characteristicsdescribed above can be incorporated natively in the multi-viewinteractive digital media representation, and provide the capability foruse in various applications. For instance, multi-view interactivedigital media representations can be used to enhance various fields suchas e-commerce, visual search, 3D printing, file sharing, userinteraction, and entertainment.

In some embodiments, a multi-view interactive digital mediarepresentation can use a series of 2-D images of a physical object takenfrom multiple viewpoints. When the 2-D images are output to a display,the physical object can appear to undergo a 3-D transformation, such asa rotation in 3-D space. This embodiment of the multi-view interactivedigital media representation approach differs from using a full 3-Dmodel of the physical object.

With a full 3-D model approach, the physical object can be representedas a series of polygons where the polygons are defined by points in a3-D model space. After the 3-D model of the physical object isgenerated, the 3-D model can be initially positioned in the 3-D modelspace. Then, the position of the 3-D model can be adjusted in 3-D modelspace as function of time. For example, the 3-D model of the physicalobject can be rotated in the 3-D model space.

The re-positioning of the 3-D model involves determining a new locationof each of the points of the 3-D model in the 3-D model space. Next,textures can be reapplied to the 3-D model. Yet further, a backgroundcan be added to the 3-D model space. Then, a light source in the 3-Dmodel space can be simulated. Finally, based upon the light source, the3-D model and the background can be re-rendered to a 2-D image. Thisprocess is repeated each time the 3-D model is changed in the 3-D modelspace.

The determination of the changes to the 3-D model positions in the 3-Dspace as a function of time, the re-texturing of the model, the additionof the background and then the re-rendering is computationallyexpensive, especially as the complexity of the 3-D model increases.Further, as described above, it requires the generation and storage of a3-D model and its defining parameters, which is time consuming. Thus,the multi-view interactive digital media representation can be morecomputationally efficient and require less memory resources than a 3-Dmodel approach.

In addition, when an apparent motion of an object is output from amulti-view interactive digital media representation, it appears as ifthe object motion is generated from an image quality 3-D textured model.Image quality 3-D textured models are generated in a time consuming andoften manual process. In particular, the generation of an image qualitytextured 3-D model of an object, such as an actual person's face, isnotoriously difficult and time consuming, especially, when a “life like”rendering of the object is desired.

In this embodiment of the multi-view interactive digital mediarepresentation approach, because of the elimination of the 3-D modelingsteps, user-selected objects from user generated 2-D images can beconverted quickly to a multi-view interactive digital mediarepresentation and then output to a display in real-time. During output,the user can control aspects of apparent motion of the object within themulti-view interactive digital media representation. Because the objectin the multi-view interactive digital media representation can begenerated from real images, such as images received from auser-controlled camera, the object appears life-like when output. In atraditional 3-D modeling approach, because of the difficultiesassociated with generating an image quality 3-D model, this capabilityis not offered.

Returning to FIG. 1, according to various example embodiments, once amulti-view interactive digital media representation 118 is generated,user feedback for acquisition 120 of additional image data can beprovided. In particular, if a multi-view interactive digital mediarepresentation is determined to need additional views to provide a moreaccurate model of the content or context, a user may be prompted toprovide additional views. Once these additional views are received bythe multi-view interactive digital media representation acquisitionsystem 100, these additional views can be processed by the system 100and incorporated into the multi-view interactive digital mediarepresentation.

With reference to FIG. 2, shown is an example of a process flow diagramfor generating a multi-view interactive digital media representation200. In the present example, a plurality of images is obtained at 202.According to various embodiments, the plurality of images can includetwo-dimensional (2D) images or data streams. These 2D images can includelocation information that can be used to generate a multi-viewinteractive digital media representation. In some embodiments, theplurality of images can include depth images 108, as also describedabove with regard to FIG. 1. The depth images can also include locationinformation in various examples.

According to various embodiments, the plurality of images obtained at202 can include a variety of sources and characteristics. For instance,the plurality of images can be obtained from a plurality of users. Theseimages can be a collection of images gathered from the internet fromdifferent users of the same event, such as 2D images or video obtainedat a concert, etc. In some examples, the plurality of images can includeimages with different temporal information. In particular, the imagescan be taken at different times of the same object of interest. Forinstance, multiple images of a particular statue can be obtained atdifferent times of day, different seasons, etc. In other examples, theplurality of images can represent moving objects. For instance, theimages may include an object of interest moving through scenery, such asa vehicle traveling along a road or a plane traveling through the sky.In other instances, the images may include an object of interest that isalso moving, such as a person dancing, running, twirling, etc.

In the present example embodiment, the plurality of images is fused intocontent and context models at 204. According to various embodiments, thesubject matter featured in the images can be separated into content andcontext. The content can be delineated as the object of interest and thecontext can be delineated as the scenery surrounding the object ofinterest. According to various embodiments, the content can be athree-dimensional model, depicting an object of interest, and thecontent can be a two-dimensional image in some embodiments.

According to the present example embodiment, one or more enhancementalgorithms can be applied to the content and context models at 206.These algorithms can be used to enhance the user experience. Forinstance, enhancement algorithms such as automatic frame selection,stabilization, view interpolation, filters, and/or compression can beused. In some examples, these enhancement algorithms can be applied toimage data during capture of the images. In other examples, theseenhancement algorithms can be applied to image data after acquisition ofthe data.

In the present embodiment, a multi-view interactive digital mediarepresentation is generated from the content and context models at 208.The multi-view interactive digital media representation can provide amulti-view interactive digital media representation. In variousexamples, the multi-view interactive digital media representation caninclude a three-dimensional model of the content and a two-dimensionalmodel of the context. According to various embodiments, depending on themode of capture and the viewpoints of the images, the multi-viewinteractive digital media representation model can include certaincharacteristics. For instance, some examples of different styles ofmulti-view interactive digital media representations include a locallyconcave multi-view interactive digital media representation, a locallyconvex multi-view interactive digital media representation, and alocally flat multi-view interactive digital media representation.However, it should be noted that multi-view interactive digital mediarepresentations can include combinations of views and characteristics,depending on the application.

With reference to FIG. 3, shown is one example of multiple camera viewsthat can be fused together into a three-dimensional (3D) model to createan immersive experience. According to various embodiments, multipleimages can be captured from various viewpoints and fused together toprovide a multi-view interactive digital media representation. In thepresent example embodiment, three cameras 312, 314, and 316 arepositioned at locations 322, 324, and 326, respectively, in proximity toan object of interest 308. Scenery can surround the object of interest308 such as object 310. Views 302, 304, and 306 from their respectivecameras 312, 314, and 316 include overlapping subject matter.Specifically, each view 302, 304, and 306 includes the object ofinterest 308 and varying degrees of visibility of the scenerysurrounding the object 310. For instance, view 302 includes a view ofthe object of interest 308 in front of the cylinder that is part of thescenery surrounding the object 310. View 306 shows the object ofinterest 308 to one side of the cylinder, and view 304 shows the objectof interest without any view of the cylinder.

In the present example embodiment, the various views 302, 304, and 316along with their associated locations 322, 324, and 326, respectively,provide a rich source of information about object of interest 308 andthe surrounding context that can be used to produce a multi-viewinteractive digital media representation. For instance, when analyzedtogether, the various views 302, 304, and 326 provide information aboutdifferent sides of the object of interest and the relationship betweenthe object of interest and the scenery. According to variousembodiments, this information can be used to parse out the object ofinterest 308 into content and the scenery as the context. Furthermore,as also described above with regard to FIGS. 1 and 2, various algorithmscan be applied to images produced by these viewpoints to create animmersive, interactive experience when viewing a multi-view interactivedigital media representation.

FIG. 4 illustrates one example of separation of content and context in amulti-view interactive digital media representation. According tovarious embodiments of the present invention, a multi-view interactivedigital media representation is a multi-view interactive digital mediarepresentation of a scene 400. With reference to FIG. 4, shown is a user402 located in a scene 400. The user 402 is capturing images of anobject of interest, such as a statue. The images captured by the userconstitute digital visual data that can be used to generate a multi-viewinteractive digital media representation.

According to various embodiments of the present disclosure, the digitalvisual data included in a multi-view interactive digital mediarepresentation can be, semantically and/or practically, separated intocontent 404 and context 406. According to particular embodiments,content 404 can include the object(s), person(s), or scene(s) ofinterest while the context 406 represents the remaining elements of thescene surrounding the content 404. In some examples, a multi-viewinteractive digital media representation may represent the content 404as three-dimensional data, and the context 406 as a two-dimensionalpanoramic background. In other examples, a multi-view interactivedigital media representation may represent both the content 404 andcontext 406 as two-dimensional panoramic scenes. In yet other examples,content 404 and context 406 may include three-dimensional components oraspects. In particular embodiments, the way that the multi-viewinteractive digital media representation depicts content 404 and context406 depends on the capture mode used to acquire the images.

In some examples, such as but not limited to: recordings of objects,persons, or parts of objects or persons, where only the object, person,or parts of them are visible, recordings of large flat areas, andrecordings of scenes where the data captured appears to be at infinity(i.e., there are no subjects close to the camera), the content 404 andthe context 406 may be the same. In these examples, the multi-viewinteractive digital media representation produced may have somecharacteristics that are similar to other types of digital media such aspanoramas. However, according to various embodiments, multi-viewinteractive digital media representations include additional featuresthat distinguish them from these existing types of digital media. Forinstance, a multi-view interactive digital media representation canrepresent moving data. Additionally, a multi-view interactive digitalmedia representation is not limited to a specific cylindrical, sphericalor translational movement. Various motions can be used to capture imagedata with a camera or other capture device. Furthermore, unlike astitched panorama, a multi-view interactive digital media representationcan display different sides of the same object.

FIGS. 5A-5B illustrate examples of concave and convex views,respectively, where both views use a back-camera capture style. Inparticular, if a camera phone is used, these views use the camera on theback of the phone, facing away from the user. In particular embodiments,concave and convex views can affect how the content and context aredesignated in a multi-view interactive digital media representation.

With reference to FIG. 5A, shown is one example of a concave view 500 inwhich a user is standing along a vertical axis 508. In this example, theuser is holding a camera, such that camera location 502 does not leaveaxis 508 during image capture. However, as the user pivots about axis508, the camera captures a panoramic view of the scene around the user,forming a concave view. In this embodiment, the object of interest 504and the distant scenery 506 are all viewed similarly because of the wayin which the images are captured. In this example, all objects in theconcave view appear at infinity, so the content is equal to the contextaccording to this view.

With reference to FIG. 5B, shown is one example of a convex view 520 inwhich a user changes position when capturing images of an object ofinterest 524. In this example, the user moves around the object ofinterest 524, taking pictures from different sides of the object ofinterest from camera locations 528, 530, and 532. Each of the imagesobtained includes a view of the object of interest, and a background ofthe distant scenery 526. In the present example, the object of interest524 represents the content, and the distant scenery 526 represents thecontext in this convex view.

FIGS. 6A to 6D illustrate examples of various capture modes formulti-view interactive digital media representations. Although variousmotions can be used to capture a multi-view interactive digital mediarepresentation and are not constrained to any particular type of motion,three general types of motion can be used to capture particular featuresor views described in conjunction multi-view interactive digital mediarepresentations. These three types of motion, respectively, can yield alocally concave multi-view interactive digital media representation, alocally convex multi-view interactive digital media representation, anda locally flat multi-view interactive digital media representation. Insome examples, a multi-view interactive digital media representation caninclude various types of motions within the same multi-view interactivedigital media representation.

With reference to FIG. 6A, shown is an example of a back-facing, concavemulti-view interactive digital media representation being captured.According to various embodiments, a locally concave multi-viewinteractive digital media representation is one in which the viewingangles of the camera or other capture device diverge. In one dimensionthis can be likened to the motion required to capture a spherical 360panorama (pure rotation), although the motion can be generalized to anycurved sweeping motion in which the view faces outward. In the presentexample, the experience is that of a stationary viewer looking out at a(possibly dynamic) context.

In the present example embodiment, a user 602 is using a back-facingcamera 606 to capture images towards world 600, and away from user 602.As described in various examples, a back-facing camera refers to adevice with a camera that faces away from the user, such as the cameraon the back of a smart phone. The camera is moved in a concave motion608, such that views 604 a, 604 b, and 604 c capture various parts ofcapture area 609.

With reference to FIG. 6B, shown is an example of a back-facing, convexmulti-view interactive digital media representation being captured.According to various embodiments, a locally convex multi-viewinteractive digital media representation is one in which viewing anglesconverge toward a single object of interest. In some examples, a locallyconvex multi-view interactive digital media representation can providethe experience of orbiting about a point, such that a viewer can seemultiple sides of the same object. This object, which may be an “objectof interest,” can be segmented from the multi-view interactive digitalmedia representation to become the content, and any surrounding data canbe segmented to become the context. Previous technologies fail torecognize this type of viewing angle in the media-sharing landscape.

In the present example embodiment, a user 602 is using a back-facingcamera 614 to capture images towards world 600, and away from user 602.The camera is moved in a convex motion 610, such that views 612 a, 612b, and 612 c capture various parts of capture area 611. As describedabove, world 600 can include an object of interest in some examples, andthe convex motion 610 can orbit around this object. Views 612 a, 612 b,and 612 c can include views of different sides of this object in theseexamples.

With reference to FIG. 6C, shown is an example of a front-facing,concave multi-view interactive digital media representation beingcaptured. As described in various examples, a front-facing camera refersto a device with a camera that faces towards the user, such as thecamera on the front of a smart phone. For instance, front-facing camerasare commonly used to take “selfies” (i.e., self-portraits of the user).

In the present example embodiment, camera 620 is facing user 602. Thecamera follows a concave motion 606 such that the views 618 a, 618 b,and 618 c diverge from each other in an angular sense. The capture area617 follows a concave shape that includes the user at a perimeter.

With reference to FIG. 6D, shown is an example of a front-facing, convexmulti-view interactive digital media representation being captured. Inthe present example embodiment, camera 626 is facing user 602. Thecamera follows a convex motion 622 such that the views 624 a, 624 b, and624 c converge towards the user 602. As described above, various modescan be used to capture images for a multi-view interactive digital mediarepresentation. These modes, including locally concave, locally convex,and locally linear motions, can be used during capture of separateimages or during continuous recording of a scene. Such recording cancapture a series of images during a single session.

With respect to FIGS. 7A to 12, system and methods for generatingmulti-view interactive digital media representations (MVIDMRs) usingangle measurements derived from sensor data are described. In moredetail, with respect to FIGS. 7A and 7B, sensor packages, theirintegration into a mobile device and sensor outputs are described. Withrespect to FIGS. 8A to 9B, rotation metrics determined from IMU data,which can be generated for a MVIDMR, are described. With respect FIGS.10A, 10B and 11, the application of rotation metrics to MVIDMRgeneration for different image capture paths are discussed. A method ofMVIDMR generation is described with respect to FIG. 12. Finally, withrespect to FIG. 13, an example of an apparatus, which can be used duringMVIDMR generation, is discussed.

FIG. 7A illustrates a sensor package 700 for determining orientation ofa camera used to generate a MVIDMR. In one embodiment, the sensorpackage 700 can include a MEMS (Micro-Electro-Mechanical System) device706. In particular embodiments, the sensor package 700 can be part of anIMU. Other types of sensor packages are possible and the example of aMEMS device 706 is provided for the purposes of illustration only.

The MEMS device 706 can include a plurality of sensors. For example, theMEMS device 706 can include a 3-axis accelerometer. The 3-axisaccelerometer can be used to measure accelerations along the z axis 702a, the y axis 702 b and the x axis 702 c. In addition, the MEMs devicecan include a 3-axis gyroscope. The 3-axis gyroscope can be used tomeasure angular velocities, 704 a (yaw) about z axis 702 a, 704 b (roll)about y axis 702 b and 704 c (pitch) about x axis 702 c. In addition, aMEMs device can include an one or more axis magnetometer (not shown),such as 3-axis magnetometer. In various embodiments, a sensor package700 can include one or more of accelerometers, gyroscopes, magnetometersor combinations thereof.

The sensor package 700 can output sensor data 708. An IMU, which caninclude a sensor processing system, such as 710, can receive the sensordata 708 and determine an orientation of a device. For example,gyroscopic data 712 can be integrated to determine angular changes aboutthe pitch, roll and yaw axes. Magnetometer data 714 can be used todetermine a heading or direction 724 relative to the Earth's magneticpoles. Accelerometer data 716 can be used to determine a direction ofthe Earth's gravity vector. Further, accelerometer data 716 can beintegrated once to determine a velocity of the device and twice todetermine distance changes.

The orientation 722 of a device relative to a reference coordinatesystem can be described with three angles, i.e., pitch, roll and yawangles. For example, the accelerometer data 716, such as from a 3-axisaccelerometer, can provide a pitch and roll orientation of a devicerelative to the Earth's gravitational vector. The magnetometer data 714,if available, can be used to provide a yaw angle. Gyroscopic data 712can be used to provide changes to the pitch, roll and yaw angles. Thus,if an initial orientation of a device is known and it begins to rotate,the gyroscopic data can be used to determine an orientation of a deviceas a function of time.

FIG. 7B illustrates a mobile device 720 with a sensor package, such asthe MEMs device 706 shown in FIG. 7A. For example, the MEMs device 706can be installed in device 720 with its axes aligned as depicted in theFIG. 7B. The device 720 can include one or more cameras (not shown)facing in the negative Z direction along axis 702 a and one or morecameras facing in the positive Z direction. An exemplary field of viewof at least one camera facing in the negative Z direction is indicatedby rays 725.

When the fields of view of two or more cameras overlap, knowledge of thedistance between the cameras can be used to obtain distance data, i.e.,the distance of the camera to objects captured in the image data. Forexample, the device 720 can include two cameras facing in the negative Zdirection with overlapping fields of view. Where the fields of viewoverlap, the distance to objects from the cameras, and hence device 720,can be estimated based upon a comparison of image data taken from bothcameras.

When device 720 is a rigid body, then based upon a position andorientation of the camera relative to the body of device 720, theorientation of the camera can be determined based upon the orientationof body of the device 720. In this example, a camera is aligned with theZ-direction at some position on the face of the body of device facing inthe negative Z direction. As described with respect to FIG. 7A, theorientation of a body of the device can be determined from the sensorpackage. Hence, based upon its position on device 720, the orientationof the camera can be derived from data from the sensor package.

In other examples, a camera can be configured so that it is not alignedwith negative Z direction, such as pointing at an angle relative to thenegative Z axis. For instance, the device 720 a first camera can bealigned with the negative Z axis and then one or more additional camerascan be configured to point at angles relative to the negative Zdirection. The light gathered from the multiple cameras can be combinedto provide a wider field of view. In another example, a camera can bedesigned to mechanically sweep through an angle to provide a wider fieldof view.

In yet another example, device 720 may not be a rigid body. For example,device 720 can include a flexible housing. When the housing is flexible,sensors may be included which measure an amount of bending. Based uponthe amount of bending determined from the sensors and data from a sensorpackage, such as a sensor package on an IMU, an orientation of thecamera on a flexible body can be determined.

FIG. 8A illustrates rotations of the mobile device 720 depicted indifferent coordinate systems. In this example, the gravity vector isinto the page. In 730, the camera of device 720, which points in thenegative Z direction, is facing toward an object 732, which is a sphere.They axis 702 b of device 720, as shown in FIG. 7B is aligned with thegravity vector.

In 730, the device 720 is moved along path 734, which is a circularpath. The circular path lies in a plane perpendicular to the gravityvector, which is into the page. As the device 720 moves, the directionof the camera, which points in the negative Z direction, is kept alignedwith a line between the center of the circle and the camera. Hence, thedirection of camera is always perpendicular to circular path 734. Forexample, this camera path can be generated if a rigid bar were attachedto the device 720 on one end and attached to a pole through the centerof the circle aligned with the gravity vector on the other end where therigid bar is allowed rotate relative to the pole in the center of thecircle.

In 730, a position of the device 720 is shown at four different times,t₁, t₂, t₃ and t₄, along path 734. The positions of the device 720 areat ninety degrees to one another. Along path 734, the camera on device720 can be used to capture images used to generate an MVIDMR of object732, which is a sphere. For example, when the device 720 follows thecomplete path 734 around object 732, the camera can capture image datawhich includes a three hundred and sixty degree view of the object 732.

In 735, the rotational motion is represented as a motion about thegravity vector, which in this example is also a rolling motion about they axis 702 b of the device because the y axis 702 b is aligned with thegravity vector in this example. When the device 720 follows the completepath 734 around object 732, the camera performs a single rotation inthis example and captures a three hundred sixty degree angular view ofthe object. Further, from t₁ to t₂, device 720 rotates ninety degreesand captures a ninety degree angular view of the object, from t₁ to t₃,device 720 rotates one hundred eighty degrees and captures a one hundredeighty degree angular view of the object and from t₁ to t₄, device 720rotates two hundred seventy degrees and captures a two hundred seventydegree angular view of the object.

In this example, the rotations of camera can be determined using datafrom the accelerometers and/or gyroscopes. For example, theaccelerometers can be used to determine a vector associated with thedirection which the camera is pointing as a function of time. An angularchange can be determined between the camera direction vectors todetermine an angular change and hence an amount of rotation from a firsttime to a second time. In another example, the rotation rates determinedfrom the gyroscopes can be integrated to determine an amount of angularchange to the camera due to the rotation of device 720. In a method,which can be referred to sensor fusion, both accelerometer data andgyroscopic data can be utilized to determine angular rotations of thecamera on device 720. In some instances, the combination ofaccelerometer and gyroscopic data can provide more accurate estimates ofthe angular change than using the accelerometer data or the gyroscopicdata alone.

In some instances, it is desirable to capture a specific angular view ofan object, such as a ninety degree view, a hundred eighty degree view ora three hundred sixty degree view. For example, a MVIDMR including athree hundred sixty view of an object may be desirable for anadvertising application. In the example of FIG. 8A, the amount ofrotation of the device 720 is related to an amount of an angular view ofthe object that is captured. Thus, a measurement of the amount ofrotation of the device 720 about an axis can be used to determine howmuch of an angular view of the object has been captured.

In the example of FIG. 8A, the movement of the device 720 is constrainedin that the negative axis is always perpendicular to the circular path734 and the y axis 702 b of the device is aligned with the gravityvector. When a person is holding device 720 to generate an MVIDMR, thismotion is difficult to reproduce. Typically, as the device 720 is movedalong a path, it can pitch, roll and yaw in a person's hand. Further,the person can move the device up and down and closer or farther awayfrom the object.

The pitch, roll and yaw can vary in opposite directions along thetranslational path. Further, the device 720 can move up and down andbackwards and forwards along a path as it is moved. In addition, thedevice can move closer or farther away from an object. These morecomplicated motions in the context of using rotational motions of thedevice 720 to determine an angular view captured are described withrespect to the following figures.

FIG. 8B illustrates roll orientations of a mobile device 720 with acamera as a function of time during image capture for MVIDMR generation.In this example, the mobile device 720 follows a circular path 744 aboutan object 732. The y axis of the device 720 is kept aligned with thegravity vector which is through the center of the object 732 and intothe page (no pitch). However, the device 720 is allowed to roll aboutthe y axis (see FIG. 7B) as if a person were holding the device 720.

Two movement sequences, 740 and 742, are shown. Each movement sequenceincludes four times, t₁, t₂, t₃ and t₄ At each time, a position of thedevice 720 along circular path 744 is shown. Further, at each time, thefollowing three things are shown, 1) the negative z axis, such as 745 a,of the device 720, 2) a field of view of the camera on device 720, asindicated by dashed lines, such as 745 b, and 3) a line, such as 745 c,from the center of the circle 744 to the origin of the negative z axis,which is perpendicular to circular path 744.

For path 740, at t₁, the negative z axis is aligned such that it isperpendicular to circular path 744. The object 732 is centered in thefield of view of the camera aligned with the negative z axis. Between t₁and t₂, the device 720 has a net clockwise rotation about the y axis(roll). At t₂, the negative z axis of the device 720 is notperpendicular to the circular path 744 and is rotated clockwise past theline which is perpendicular to circular path 744. This orientation ofthe camera results in the object 732 not being centered in the field ofview of the camera.

Between t₂ and t₃, the device 720 has a net clockwise rotation about they axis (roll). At t₃, the negative z axis of the device 720 is again notperpendicular to the circular path 744 and is rotated clockwise suchthat it has not reached line which is perpendicular to the circular path744. Thus, again, this orientation results in the object 732 not beingcentered in the field of view of the camera.

In general, a user can use the display of device 720 to try to keep thecamera centered on the object 732. However, as the user moves the cameraabout the object 732, along a path, such as 744, the user may overcorrect or under correct as they try to keep the object centered in thedisplay of device 720. Hence, the user can rotate the devicecounter-clockwise and clockwise to keep the object centered.

Typically, for determining the angular rotation amount of camera asrelated to an angular view captured of an object, such as object 732,the angular rotation amount in a particular rotation direction can beused. For example, in FIG. 8B, the angular view of the object 732, whichcaptured, can be related to amount of rotation of device 720 in theclockwise direction. The rotation in the counter-clockwise directiondoesn't contribute to the angular view of the object which has beencaptured along path 744. Thus, in one embodiment, when consideringangular changes about an axis, such as the roll axis, only the angularchanges in one direction may be considered, such as only rotations inthe clockwise direction or only rotation in the counter-clockwisedirection (If in 740, the device 720 moved in the counter-clockwisedirection about path 744, then rotations only in the counter-clockwisedirection about the roll axis can be of interest in regards todetermining the angular view captured of object 732 and angular changesin the clockwise direction can be ignored.)

Between t₃ and t₄, the device 720 is not rotated about the y axis (zeroroll), i.e., it is in the same roll orientation at t₄ as it at t₃. As aresult, the object 732 moves out of the field of view of the camera. Theobject moving out of the field of view of the camera can be detected inthe image data from the camera. In this instance, the MVIDMR system canbe configured to detect the object 732 has moved out of the field ofview of the camera.

As described above, an angular view of a particular amount of an object,such as 732, may be desired. When an object, such as 732, moves out ofthe field of view, the MVIDMR system perform one or more of thefollowing: 1) stop the image capture process, 2) stop the determinationof the amount of the angular view of the object captured and 3) outputto the user a notification that the image capture process for the MVIDMRhas stopped. In one embodiment, the MVIDMR system can be configured tooutput to the user the amount of angular view of the object, which hasbeen captured, prior to the object moving out of the field of view.

In a further embodiment, the MVIDMR system can indicate the user tostart the acquisition process again from the beginning, such as goingback to t₁. In another embodiment, the MVIDMR system can be configuredto direct the user to bring the object 732 back into the field of viewof the camera and restart the image process. Then, the MVIDMR system canagain begin to determine the changes to the angular position of thecamera on device 720 to determine an amount of angular view of theobject, such as 732, which has been captured.

In yet another embodiment, the MVIDMR system can be configured to directa user to a previous position and then specify an orientation of thecamera. For example, the MVIDMR system can be configured to outputdirections, such as a guide, for the user to return the camera to theposition at t₃. When the user returns to the position, the MVIDMR systemcan direct the user to a specific orientation of the camera. Forexample, meters or guides can be output that indicate that the device720 is in a desired orientation. Then, MVIDMR system can indicate to theuser, such as via a message to the display, to again begin the MVIDMRimage capture process, such as the user walking along path 744. Afterthe image capture process is initiated, the MVIDMR system can againbegin to determine angular changes associated with the device 720 andits camera and determine an amount of angular view of the object, suchas 732, which has been captured.

FIG. 8C illustrates rotations 750 of the device 720 about the gravityvector for the movement sequence 740 in FIG. 8B. In this example, thegravity vector is aligned with the y axis of device 720. The directionof the camera at each time is indicated by an arrow. A circle 752 in aplane perpendicular to the gravity vector is shown in FIG. 8C to betterillustrate the amount of angular rotation about the gravity vector ofthe camera on the device 720. As described above, the angular rotationsof device 720 can be determined using sensor data from a sensor packageon an IMU, such as accelerometer data and/or gyroscopic data.

Between time t₁ and t₂, the device 720 rotates a first angular amount752. Between time t₂ and t₃, the device 720 rotates a second angularamount 754. Between time t₃ and t₄, the device 720 doesn't rotate. Theangular change 752 is greater than the angular change 754. As shown inFIG. 8B, the angular change 752 is an overestimation of the angular viewcaptured. However, between t₁ and t₃, the total angular change is thesum of angles 752 and 754. As can be seen in FIG. 8B, the sum of angles752 and 754 give a better estimate of the angular view captured of theobject 732.

Returning to FIG. 8B, a second movement sequence of device 720 alongpath 744 is shown. If the device 720 is moved in a clockwise directionalong the path 744, then the device 720 needs to be rotated in aclockwise manner to keep the object 732 in the field of view of thecamera as it moves. In the second movement sequence, at t₁, thedirection of the camera on device 720 is perpendicular to path 744.Then, at t₂, the device 720 is rotated clockwise, but, its positionalong the path 744 remains fixed. Next, at t₃, the device is rotatedcounterclockwise, but, its position along the path 744 again remainsfixed. Finally between t₃ and t₄, the device is rotated clockwise and itis moved to a new position along path 744.

FIG. 8D illustrates rotations 760 of the device 720 about the gravityvector for the movement sequence 742 in FIG. 8B. In this example, thegravity vector is aligned with the y axis of device 720. The directionof the camera at each time is indicated by an arrow. A circle 762 in aplane perpendicular to the gravity vector is shown in FIG. 8D to betterillustrate the amount of angular rotation about the gravity vector ofthe camera on the device 720. As described above, the angular rotationsof device 720 can be determined using sensor data, such as accelerometerdata and/or gyroscopic data, from a sensor package on an IMU.

In FIG. 8D, the angular change of device from t₁ to t₂ is indicated byangle 768. The angular change from t₂ to t₃ is indicated by angle 766.The angular change from t₃ to t₄ is indicated by angle 764. Finally, theangular change from t₂ to t₄ is indicated by angle 770.

The rotations of device 720 from time t₁ to t₂ and from t₂ to t₃ don'tcontribute to the angular view of the object 732 captured by the camera.In this example, the rotation angle from t₁ to t₄, which is angle 772,is closest to the angular view captured by the object on the movementsequence 742. In one embodiment, both the positive and negativerotations can be counted towards a rotational angle total where thedirection change results in a sign change. For example, the sum ofangles 764 and 768, which can be considered a positive rotation and theangle 766, which can be considered a negative rotation, results in angle772, which approximates the angular view of the object captured.

In another embodiment, only the positive rotations can be counted. Thus,angle 764 and 768 can be summed and angle 766 can be ignored. Thisapproach overestimates the angular view of the object captured.

In yet another embodiment, the change in angle of the vector in thedirection of the camera as a result of a negative rotation can beignored and the angular rotation change in the positive direction can bemeasured from the position of the direction vector of the camera priorto the negative rotation. For example, in FIG. 8D, rather than measuringthe angular change from t₃ to t₄, based upon, the position of thedirection vector of the camera at time t₃, which provides angle 764, theposition of the direction vector at t₂ can be used. This measurementprovides angle 770. Thus, the total change in the rotation angle from t₁to t₄ is the sum of angles 768 and 770, which is equal to angle 772.This approach for determining the rotation of the camera can produce areasonable approximation of the angular view of the object which hasbeen captured.

In one embodiment, the MVIDMR system can be configured to receive aninput of the direction that the camera will be travelled around anobject, such as 732 in FIG. 8B. For example, the system can beconfigured to receive an input of a clockwise or counter clockwisedirection. Based upon, this input, the system can determine whichrotations are to be considered positive rotations and which rotationsare to be considered negative rotations.

In another embodiment, the MVIDMR system can be configured to determinea direction of motion that is being used to create the MVIDMR. Forexample, the MVIDMR system can be configured to identify one or moretracking points on an object in an image using the pixel data from theimage. The tracking point can move in the pixel data as the camera ismoved. Based upon, the direction the tracking points are moving in thepixel data, a positive and negative rotation direction can be determinedfor the purposes of determining the angular view of an object that hasbeen captured.

In yet another embodiment, the initial movements of the direction vectorassociated with the camera as determined from the IMU data can beexamined. Based upon the movements of the direction vector at the startof the MVIDMR acquisition process, the direction of rotation in whichthe camera is generally moving can be determined. The direction ofrotation the camera is generally moving can be considered a positiverotation for the purposes of determining the amount of angular rotationof the camera along path. In yet further embodiment, the movement oftracking points from the pixel data and the initial changes to thedirection vector associated with the camera can be used to determine inwhich direction the camera will rotate during acquisition of image datafor the MVIDMR.

Next, examples are considered where the device 720 is allowed to movegenerally in 3-D space. FIG. 9A illustrates pitch and roll of a mobiledevice 720 and angle changes as a function of time relative to thegravity vector during image acquisition for MVIDMR generation. Thedirection of the gravity vector is indicated by 802 a. An orthogonalcoordinate system associated with the gravity vector is indicated by 802b and 802 c.

The direction of the body centered coordinate system for device 720 isindicated by 804 a, 804 b and 804 c. The direction of the camera is inthe negative Z direction as in the previous pictures. The pitch and rollorientation of the device 720 relative to the gravity vector can bedetermined using sensor data from the 3-axis accelerometer. As describedabove, if a magnetometer data is available, then it may be possible toobtain yaw data.

The gyroscopic data can be used to determine a roll rate of the device720 about axis 804 b and the pitch rate about 804 c. The roll rate canbe integrated to obtain an amount of roll between a first time and asecond. The pitch rate can be integrated to obtain an amount of pitchbetween a first time and a second time.

In one embodiment, the angular rotation amount of device 720 during anMVIDMR image acquisition can be determined using just the roll rate orpitch rate. If the device is orientated in a portrait mode and the userplans to pan around an object with this orientation, then the roll ratefrom the gyroscopic data as a function of time can be integrated todetermine a total roll angle amount as a function of time. In oneembodiment, negative roll rates can be ignored for the purposes ofdetermining the total roll angle amount. The total roll angle amount asa function of time can be used to estimate the angular view of an objectthat has been captured during image acquisition.

If the device 720 is orientated in a landscape mode and the user plansto pan around an object with the device in this orientation, then thepitch rate from the gyroscopic data as a function of time can beintegrated to determine a total pitch angle as a function of time. Inthis example, negative pitch rates can be ignored for the purposes ofdetermining the total pitch angle amount. The total pitch angle amountas a function of time can be used to estimate the angular view of anobject that has been captured during the image acquisition process.

In one embodiment, the MVIDMR system can present a user with a selectionof a type of path for the device to follow and an orientation of thedevice that is to be used during the path. Based upon the input providedby the user, the MVIDMR system can determine whether to determine thetotal pitch angle amount or the total roll angle amount for the purposesof determining an angular view amount of an object that has beencaptured as a function of time. In these embodiments, as roll rate dataand pitch rate data is being integrated, the orientation of the deviceas a function time may not be needed. However, a starting time to beginthe integration of the roll rate data or the pitch rate data and anending time may have to be determined. In one embodiment, the start andstop can be determined based upon a user selecting a button in an inputinterface, i.e., the user can select a button to start the image captureand end the image capture.

In another embodiment, the sensor data from the 3-axis accelerometer canbe used. The 3-axis accelerometer can be used to determine a roll andpitch orientation of the device 720 relative to the gravity vector as afunction time. For example, in FIG. 9A, the device is pitched by angle808 about the g_(x) axis 802 c and rolled about the gravity vector g_(z)802 a by an angle amount 806 at time t₁. The yaw angle amount about theg_(y) axis 802 b is not determined using the 3-axis accelerometer data.As described above, it can be set to an arbitrary value such as zerodegrees.

At t₁, the first value of angles 806 and 808 provide an orientation ofthe Z axis 804 a (or negative Z axis) in the coordinate systemassociated with the gravity vector (802 a, 802 b and 802 c). Asdescribed above, a camera on device 720 can be orientated in thenegative z direction. At t₂, the magnitude of the value of the pitchangle 808 can increase or decrease relative to its value at t₁ and themagnitude of the value of the roll angle 806 can increase or decreaserelative to its value at t₁. The values of the pitch angle 808 and rollangle 806 at time t₂ again determine the orientation of the negative zvector in the coordinate system associated with the gravity vector.

In one embodiment, at different times, such as between t₁ and t₂, anangle value can be determined between the 3-D camera direction vectors,which is the negative z direction in the camera based coordinate system.In this example, the 3-D camera direction vector at each time can bedetermined in the gravity based coordinate system (802 a, 802 b and 802c) using the pitch and roll angles about the g_(x) 802 c and g_(z) 802 aaxes obtained from the accelerometer data. The yaw angle about the g_(y)802 b vector can be set to zero or some other fixed value (no yaw changeas a function of time). With pitch, roll and yaw angles in the gravitybased coordinate system for 3-D camera vector known as a function oftime, the change in the angle between the 3-D camera direction vector attwo different times, such as between times, t₁ and t₂, can bedetermined.

The angle changes can be summed to determine a total angle change as afunction of time. The angle change is approximately around the gravityvector g_(z) 802 a. The total change in angle can be used to estimate anangular view of an object captured by the camera. Thus, the angular viewof the object captured as function of time can be determined and outputto a display screen. Like the examples described above, a rotationdirection that is needed along the path to keep the object in view ofthe camera can be determined, i.e., clockwise or counter clockwise.Further, as described above, angle changes, in the direction that is notneeded, can be ignored for the purposes of determining the angularrotation amount in the rotation direction that is needed to keep theobject in view of the camera.

In another embodiment, the angle changes can be projected into aparticular plane. For example, a circle 812 is shown in a planeperpendicular to the gravity vector. The 3-D camera direction vector canbe projected into this plane. Then, the angle changes of the 3-D cameradirection vector projected into this plane from time to time can bedetermined, such as 810. Like the examples described above, a rotationdirection that is needed along the path to keep the object in view ofthe camera can be determined, i.e., clockwise or counter clockwise.Further, as described above, angle changes in the plane in the directionthat is not needed can be ignored.

The determination of angle changes about the gravity vector g_(z) or ina plane perpendicular to the gravity vector can be useful when a personwalks a camera around an object to generate an MVIDMR because the pathis approximately perpendicular to the gravity vector. However, in otherembodiments, other camera paths around an object that are notperpendicular to the gravity vector can be used. Thus, a determinationof the change in rotation angles about the gravity vector may notprovide a good estimate of the angular view of the object which iscaptured in the MVIDMR.

In these instances, it may be desirable to allow a specification of anaxis about which to determine angle changes during MVIDMR generation.FIG. 9B illustrates pitch and roll of a mobile device and angle changesas a function of time relative to an arbitrary axis during MVIDMRgeneration. In FIG. 9B, an axis 824 a is shown. The axis 824 a can bethrough an object for which images are being captured. The axis 824 acan be specified in a fixed orientation relative to the gravitycoordinate system, such as via three fixed rotation angles. The threefixed rotation angles specify a coordinate system transformation fromthe gravity based coordinate system to a coordinate system associatedwith the arbitrary axis.

The axis 824 a can be used to define an orthogonal coordinate systemincluding axes 824 b and 824 c. Based upon the coordinate transformationbetween the orthogonal coordinate system associated with the axis 824 aand the pitch angles and roll angles determined in the gravity basedcoordinate system, pitch and roll angles for the 3-D camera directionvector can be determined in the coordinate system associated with axis824 a. In this example, the roll 822 is specified about axis 824 a andthe pitch is specified about axis 826.

Using the pitch and roll angles in the coordinate system associated withaxis 824 a, angular changes for the 3-D camera direction vector can bedetermined about axis 824 a. Similar to the method described above, theangular changes about axis 824 a can be determined in a planeperpendicular to axis 824 a. For example, circle 830 can be in a planeperpendicular to axis 824 a and the angle change as a function of time,such 832, can be determined in this plane. Again, the angular changescan be used to estimate an angular view captured of an object duringMVIDMR generation.

In one embodiment, a camera path, rather than about the roll axis, suchas 824 a, can be generated about the pitch axis, such as 824 c. Thus,angular changes can be determined about the pitch axis, such as 824 c.Similar to the method described above, the angular changes about axis824 c can be determined in a plane perpendicular to axis 824 c. Forexample, circle 828 can be in a plane perpendicular to axis 824 c andthe angle change as a function of time, such 834, can be determined inthis plane. Again, the angular changes can be used to estimate anangular view captured of an object during MVIDMR image acquisition, suchas an object through which axis 824 a passes.

Next, with respect to FIGS. 10A, 10B, 11A and 11B, some examples ofangular view estimation of an object applied to a number of differentexamples of camera paths are described. FIG. 10A shows a path 914 of adevice 900 with a camera during image capture for an MVIDMR of a person904. In this example, the device 900 can generally follow circular path912 such that some angular view of person 904 is captured. While thedevice 900 generally follows circular path 912, on its actually path914, the device 900 can do one or more of the following: 1) move up anddown, 2) pitch, roll and yaw and 3) move closer or father away from theperson 904.

In this example, the person 904 is standing on the ground. The gravityvector 910 is indicated by an arrow. An orthogonal coordinate system 905can be associated with the gravity vector 910. In this example, thecircular path 912 lies in a plane which is perpendicular to the gravityvector 910.

Based upon a sensor package on the device 900, at least a pitch and rollangle orientation of the device 900 can be determined in coordinatesystem 905 as a function of time (e.g., using 3-axis accelerometerdata). Based upon, the pitch and roll angle orientation of the device900 as a function of time, a change in angle about the gravity vectorcan be estimated from time to time. As described above, the anglechanges can be used to estimate a total angle change and hence theamount of the angular view of an object captured in the camera imagesassociated with the MVIDMR as a function of time.

As described above, a number of tracking points, such as 902, 905, 906 aand 906 b on the person 904 can be determined from the image data. Thetracking points can change as a function of time. If the mobile deviceis not sufficiently rotated during the image capture process as it movesalong path 914, then the person 904 can move out of the image and thetracking points will be lost.

FIG. 10B shows a path 930 of a device with a camera during image capturefor an MVIDMR of a cup and saucer 922. In this example, the device asindicated by the arrow 932 can generally follow circular path 930 suchthat some angular view of person cup and saucer 922 is captured. Again,while the device generally follows circular path 930, on its actuallypath 914, the device 900 can do one or more of the following: 1) move upand down, 2) pitch, roll and yaw and 3) move closer or father away fromthe person cup and saucer 922.

In this example, the cup and saucer 922 can be resting on an object suchas a table. The gravity vector 910 is indicated by an arrow. Anorthogonal coordinate system 905 can be associated with the gravityvector 910. In this example, axis 934 has been specified. As describedabove, the axis 934 can be specified in the gravity based coordinatesystem 905. Angle changes can be computed about axis 934. The path 930is approximately perpendicular to axis 934. As described above, anglechanges can be projected into the plane associated with path 930.

Based upon a sensor package on the device with a camera, such as asensor package associated with an IMU, at least a pitch and roll angleorientation of the device can be determined in coordinate system 905 asa function of time. As previously described above, based upon, the pitchand roll angle orientation of the device as a function of time, a changein angle about the axis 934 can be estimated from time to time using anumber of different methods. As described above, the angle changes canbe used to estimate a total angle change and hence the amount of theangular view of an object captured in the camera images associated withthe MVIDMR.

As described above, a number of tracking points, such as 924 and 926 onthe cup and saucer can be determined from the image data. The trackingpoints can change as a function of time. If the mobile device is notproperly rotated during the image capture process as it moves along path930, then the cup and saucer 922 can move out of the image and thetracking points will be lost. As described above, when tracking of anobject is lost, the MVIDMR generation process can be stopped.

FIG. 11A and 11B illustrate another example of angular view estimationduring image capture associated with a MVIDMR. Initially, an axis 1014is specified through object 1000, which is a statue. A possible camerapath is indicated by curved path 1010. The path 1010 is approximatelyperpendicular to axis 1014. An angular view range 1012 is specified by astarting line 1006 and an ending line 1008. In one embodiment, a MVIDMRsystem can be configured to receive a desired angular view for an objectin an MVIDMR, such as a value amount in degrees, and then generate thepath 1010, the starting point 1006, the ending point 1008 and theangular view range 1012.

In another embodiment, the MVIDMR system can receive image data of theobject 1000 from the camera. The image data can be output to a displayon a device including the camera. The MVIDMR system can be configured togenerate an input interface on the display which allows a specificationof an axis about which to determine angle changes and a desired angularview amount of the object. In response, to receiving a specification ofan axis around which to rotate, i.e., axis 1014, and an angular viewrange, the MVIDMR system can augment the image data with the path 1010,the angular view range 1012, the starting point 1006 and the endingpoint 1008. The augmented image data can be output to a display on thedevice.

In one embodiment, a line can be output to a touchscreen displayinterface. The line can be used to represent an axis about which todetermine rotations. Via the touch screen display interface, a user maybe able to position the line relative to an object appearing in imagesoutput to the touch screen display interface, such as an object forwhich an MVIDMR is to be generated. Via the interface, the user may alsobe able to specify a rotation direction for the camera about the axis,such as clockwise or counterclockwise.

In one embodiment, the starting point 1006 and the ending point 1008 canbe adjustable. For example, the MVIDMR system can be configured toreceive an input that allows the starting line and the ending linelocations to be adjusted by a user along path 1010. Thus, the magnitudeof the angular view which is to be captured can be changed. Further, theportion of the object in the angular view range can be adjusted. Forexample, when a ninety degree angular view of an object is desired, thepositions of the starting and the ending lines can be adjusted to selecta ninety degree view segment of the object.

In addition, one or more tracking points, such as 1004 a, which resideon the object, can be determined. An indicator showing the trackingpoint locations can also be generated and added to the image data. Inone embodiment, one or more of the tracking point locations determinedon an object or objects appearing in the image data can be selectable inan interface, such as a touch screen interface. A selection of one ofthe tracking points can be used to select an object appearing in theimage data upon which to generate an MVIDMR.

In addition, the MVIDMR system can output to the display an indicator,such as 1002 a. The indicator can provide an indication of how much ofan angular view of the object has been captured. In FIG. 11A, thecapture process has not started. Hence, the indicator 1002 a indicates azero value.

In FIG. 11B, a second state of the MVIDMR acquisition process isillustrated. The camera position has changed. Hence, the tracking point1004 b is at a different location on the object and the view of theobject has changed.

In the second state, the device with the camera has progressed toposition 1018 along curve 1010. The position 1018 is more than half waythrough the angle range between the starting line 1006 and 1008. Asecond state 1002 b of the progress indicator shows a current amount1022 of the angular view of the object 1000 which has been captured.When the camera moves to position, such that the ending line 1008 isreached, the MVIDMR system can output an indication that the captureprocess has been completed.

In one embodiment, object recognition and/or point tracking in the imagedata can be used as a source for angle information. For example, when athree hundred sixty degree view of an object is being captured, afeature and/or tracking point in the initial images can be re-recognizedas three hundred sixty degrees is approached. Then, a three hundredsixty degree angular view value can be associated with the image wherethe recognized feature repeats itself

In one embodiment, an angular view value determined from featurerecognition in an image can be used for calibration purposes. Forexample, if the angular view value determined from the featurerecognition is greater or less than the value determined from the IMUdata, then the value determined from the feature recognition can be usedto scale the value from the IMU data. For instance, if the angular viewvalue determined from the IMU data is three hundred seventy degrees andthe value determined from feature recognition value is three hundredsixty degrees, then, to calibrate with the feature recognition value,the IMU determined angular values can be scaled by the ratio of threehundred sixty degrees divided by three hundred seventy degrees.

In another example, some understanding about the basic geometry of anobject can be used to allow angular view associated with an object to bedetermined. For instance, it may be possible to distinguish theboundaries of a side of a car versus a front or a back of a car. Themaximum length of the side of the car appearing in an image can occurwhen the camera is approximately perpendicular to the side of the car.The maximum width of the front of the car can occur when the camera isapproximately perpendicular to the front of the car. Thus, if the lengthof the side car is tracked in the image data, a width of the front ofthe car is tracked in the image data and the camera is moving from theside to the front of the car, then the angular view value between theimage where the maximum length of the side of the car occurs and theimage where maximum width of the front of the car occurs is about ninetydegrees.

The angular view value from the IMU data between the image where themaximum length of the side of the car occurs and the image where maximumwidth of the front of the car occurs can also be determined. Forexample, the angular view value between these images estimated from theIMU data can be about ninety three degrees. In one embodiment, theangular view value based upon feature recognition can be used tocalibrate angular values determined from the IMU data. For example, theangular view values determined from the IMU data can be multiplied bythe ratio of ninety degrees to ninety degrees to calibrate the IMU data.In general, using feature recognition, if an angular view value can bedetermined for an image or an angular view value change can bedetermined between images, then the information obtained from featurerecognition can be used to calibrate the IMU data.

FIG. 12 illustrates an example of a process flow 1100 for generating aMVIDMR using IMU data. In 1102, a request to generate an MVIDMR can bereceived. For example, the request can be received via a touch screeninterface on a mobile device or verbally via a microphone on a mobiledevice. In 1104, an angle value can be optionally received. The anglevalue can be used to specify an angular view of an object that isdesired in the MVIDMR.

In 1106, the MVIDMR can optionally receive a specification of an axisabout which to determine angle changes as a function of time. In oneembodiment, the default axis is the gravity vector. In 1108, the MVIDMRsystem can be configured to optionally output to an interface, such as atouch screen interface, a plurality of angle estimation methods whichcan be utilized. A number of different methods have been describedabove, such as using gyroscopic data or accelerometer data. Via theinterface, a selection of one of the methods can be received.

In 1110, initial IMU data, such a data which allows a current tiltorientation of a mobile device relative to the Earth's gravity vector,can be determined. In addition, live image data can be received from acamera, such as a camera on a mobile device. In 1112, based upon the IMUdata, an initial orientation of the device including the camera can bedetermined.

As the device orientation changes, the orientation of the device andhence the camera can be determined as a function of time. In 1116, basedupon the orientation of the device including the camera as a function oftime, the angle change can be determined from time to time. In 1118, thetotal angle change as a function time can be determined. The total anglechange as a function of time can be associated with the live image datawhich is being captured. Hence, each image in the sequence of imagesthat has been received can be associated with an amount of the angularview of the object that has been captured previously.

In 1120, during the image gathering process, the angle changes from timeto time and/or the total angular view of the object which has beencaptured can be output to a display. An indication can be generated whena desired angular view of the object has been captured. Further, theimage capture process can end. Then, in 1122, the MVIDMR with thedesired angular view can be generated.

In one embodiment, when an angular view of an object of some amount iscaptured, the MVIDMR system can be configured to generate an MVIDMR withan angular view that is equal to or less than angular captured of theobject. For example, when a three hundred sixty degree view of an objectis captured, the system can be configured to receive an input of angularview amount less than three hundred sixty degrees and a range, such asone hundred degrees starting at ten degrees and going to one hundred andten degrees or ninety degrees starting at one hundred eighty degrees andgoing to two hundred seventy degrees. In this example, the startingpoint where images are first captured can be considered zero degrees.

FIG. 13A illustrates a setup 1200 for generating an MVIDMR including athree hundred sixty degree of an object 1212, which is a shoe. In thisexample, a mobile device 1202 includes a display 1206, an IMU and atleast a front facing camera. A shoe 1212 is resting on a platform 1214.The device can also include components, such as a rear facing camera, amicrophone, input buttons, a process and memory, wireless communicationinterfaces, power interfaces and memory storage devices.

The mobile device 1202 is orientated in a landscape mode. Alternatively,the mobile device can be orientated in a portrait mode. The landscape orportrait mode can be generally maintained during the image capture andselection process. The front facing camera captures an image 1204including the shoe 1212. The image 1204 including the shoe 1212 isoutput to the display 1206. The initial distance from camera to the shoe1212 is indicated by line 1216.

A MVIDMR generation process can be initiated with the mobile device 1202in the position shown in set up 1200. After the MVIDMR generationprocess is initiated, the mobile device 1202 can start in its initialposition and then move along a path, such as 1210. As described above,to keep the object 1212 on the display 1206 of the mobile device, themobile device 1202 can rotate along the path.

In this example, the path 1210 is shown as circular and lies in a planewhich is perpendicular to the gravity vector 1208. When the device 1202is hand held, while moving along its path, the height of the deviceabove the ground and its distance 1216 from the object 1212 can vary.Further, the device 1202 can pitch, roll and raw as previously describedas it moves along its path, such as 1210.

In one embodiment, a path can involve the mobile device 1202 movingaround the object 1212, such that a three hundred sixty degree angularor more view of the object is captured. Alternately, less than threehundred sixty degree angular view of the object can be captured. Inanother embodiment, the device 1202 can be held in place and the object1212 can be rotated. For example, a person can twirl around. However, inthis instance, the IMU data would not record a rotation of device 1202.

When more than three hundred and sixty degrees of the object is capturedin an image sequence for an MVIDMR, such as 1212, is captured, the imagesequence can be trimmed down to three hundred and sixty degrees. Duringtrimming, the angular view amounts associated with the images,determined from the IMU data, can be used to determine where the overlapoccurs in the sequence of images. The trimming can be performed from thebeginning or the end of the sequence of images. For example, if threehundred sixty five degrees is captured images, images can be trimmedfrom the first five degrees predicted from the IMU data or from the lastfive degrees.

In another embodiment, the overlapping images can be merged. Forexample, the images from the last five degrees can be merged with theimages from the first five degrees. An order of the merged images can bebased upon the determined angular view amounts. For example, imagesbetween three hundred sixty degrees and three hundred sixty one degreescan be merged with the images between zero and one degrees. Inparticular, an image with an estimated angular view 360.5 degrees can beplaced between images with estimated angular view of 0.25 degrees and0.75 degrees.

In general, a sequence of images with a determined angular view rangecan be trimmed within the estimated angular view range. For example, asequence of images with a determined angular view range of three hundredsixty degrees can be trimmed down to a range between zero and onehundred eight degrees, between ninety and two hundred seventy degrees,between seventy and two hundred seventy degrees, etc. The MVIDMR systemcan be configured to output an angular view range of an object capturedin a sequence of images, receive an input of an angular range to trimthe sequence of images, trim the sequence of images in accordance withthe received angular range and then generate based upon the trimmedsequence of images.

While the device 1202 moves along the path along the live images can bereceived from the camera. As described above, a sequence of images canbe selected from the live images. The sequence of images can be used togenerate an MVIDMR At the beginning of the sequence, the estimatedangular view captured of the object, such as 1212 can be zero. Then, theestimated angular view of the object 1212 can increase as the object,such as 1212, is circumscribed. These estimates of the angular view ofthe object can be associated the images in the sequence of images.

In one embodiment, all of the images and the estimated angular view ofthe object associated with each image can be stored. Then, later, anangular spacing can be specified. For example, an angular spacing ofimages can be five degrees. Based upon, the specified angular spacing, asubset of images with the desired spacing can be selected from among thestored images using the angular view estimates for each stored image togenerate a new sequence of images. The, new sequence of images can beused to generate an MVIDMR.

In another embodiment, an angle profile can be specified (see FIG. 14).An angle profile can specify angular values. For example, a constantangular spacing value can be specified, such as three degrees or fivedegrees. Based upon the constant angular spacing value, angular valuesin the angle profile can be determined. A constant angular spacing canbe selected to provide a smoother viewing experience when the MVIDMRgenerated from the selected images is played back.

As the live images are received, the current value of the angular viewcan be determined. The current value of the angular view can be comparedto angular values in the angle profile. When the current value of theangular is proximate to an angular value in the angle profile, e.g.,plus or minus a quarter of degree, plus or minus a half a degree, plusor minus a degree, plus or minus two degrees, etc., one or more of thelive images can be selected and can be stored. The selected live imagescan provide a sequence of images that can be used to generate an MVIDMRof the object, such as 1212. These two embodiments are furtherillustrated with respect to FIG. 13B as follows.

FIG. 13B illustrates live images 1220 selected for use in an MVIDMRbased upon angle estimation using IMU data. A plurality of images suchas 1222 a, 1222 b, 1222 c, 1222 d, 1222 e, 1222 f, 1222 g, 1222 h, 1222i, 1222 j, 1222 k, 1222 l and 1222 m, selected from among the liveimages received from the camera on mobile device 1202, are shown. Theselected images span almost a three hundred sixty degree view of object1212.

The angular view of the object estimated from the IMU data andassociated with each of the selected images is indicated as variousprogressions around a circle, such as 1224 a, 1224 b, 1224 c, 1224 d,1224 e, 1224 f, 1224 g, 1224 h, 1224 i, 1224 j, 1224 k, 1224 l and 1224m. Two examples of angular spacing, 1226 a and 1226 b, between images1222 a and 1222 b and 1222 b and 1222 c are shown. As described above,the angular spacing can be associated with angles described in an angleprofile. For example, when the determined angular view 1224 a isapproximately equal to angle 1226 a and angle 1226 a is in the angleprofile, then image 1222 b can be selected. In another example, when thedetermined angular view 1224 b is approximately equal to the sum ofangles 1226 a and 1226 b and the sum of angles 1226 a and 1226 b is inthe angle profile, then image 1222 c can be selected.

As three hundred sixty degrees is approached image 1222 m is selected.In one embodiment, when equal angular spacing between images is desired,the last image, such as 1222 m may not be utilized. Instead, image 1222a can be used. Thus, an MVIDMR using the images can cycle from image1222L to image 1222 a instead of 1222L to 1222 m to 1222 a when theMVIDMR cycles from its end at three hundred sixty degree to itsbeginning at zero degrees. This approach may provide a smoothertransition. In another embodiment, image 1222 m can be utilized. Thus,the MVIDMR can cycle from image 1222 m to image 1222 a or vice versawhen the MVIDMR using the images is played back.

Next, some example angle profiles are discussed. FIG. 14 illustratesdifferent angle profiles for selecting live images for use in an MVIDMR.A selected image at an angle value is indicated by a rectangle. A fewexample images appear above some of the rectangles. Angles up to onehundred eighty degrees are considered. However, as described above, afull three hundred sixty degrees can be considered.

Further, angles above three hundred sixty degrees can be considered. AnMVIDMR can include multiple rotations about an object. In a firstrotation about an object, a first angular spacing can be used to selectimages. In a second rotation about an object a second angular spacingcan be used to select images. The different angular spacing and theangle ranges to apply the angular spacing can be specified in the angleprofile. For example, in the first rotation, a five degree angle spacingcan be used between images based upon the angle estimation from the IMUdata. Whereas, in the second rotation, a ten degree angle spacing can beused between images based upon the angle estimation from the IMU data.

In 1400, based upon the angle estimation from the IMU data, imagesestimated to be five degrees apart are selected. In 1410, based upon theangle estimation from the IMU data, images estimated to be ten degreesapart are selected. As described above, in one embodiment, only imagesselected from among the live images using the angle criteria areselected. Thus, in 1400, only images determined to be about five degreesapart can be saved and, in 1410, only images determined to be about fivedegrees apart can be saved.

In another embodiment, all or a portion of the live images including anangular view value determined for each image can be stored. Then, latera sequence of images from the saved images can be selected based upon anangle criterion specified in the angle profile. For example, live imagescan be selected initially selected at a particular angular increment,such as half degree increments, and/or at a reduced frame rate, such asten frames per second. Subsequently, images at five degree incrementscan be selected from among the saved images to provide image sequence1400 or 1410. Then, image sequence 1400 or 1410 can be used to generatean MVIDMR.

In 1420, the spacing used to select images varies between five and tendegrees. Five degrees increments are used around forty five degrees andone hundred thirty five degrees and a ten degree spacing is usedelsewhere. When a MVIDMR is generated using image sequence 1420, theclustering of images can cause the rotation rate to appear to slow downaround where a smaller five degree angle increment is used. This effectcan be used to bring to a user's attention a particular angular view ofan object.

In image sequence 1430, a five degree spacing is used for the firstninety degrees of angular view of the object and ten degrees is used forthe second ninety degrees. Thus, when used in an MVDMR, the first ninetydegrees may play back slower than the second ninety degrees. In general,the spacing doesn't have to be constant and can vary from image toimage. Thus, an irregular spacing pattern can be employed to selectimages.

FIG. 15 illustrates an example of a process flow 1500 for generating aMVIDMR using IMU data for image selection. In 1502, a request togenerate an MVIDMR can be received. For example, an interface can begenerated on a mobile device, such as a touch screen interface or voiceinterface. Via touch screen interface or a voice interface, a commandcan be received to start the MVIDMR generation process.

In 1504, in one embodiment, an angular view value can be received. Theangular view value can be used to determine when to stop selectingimages for the MVIDMR. For example, a value of ninety, one hundredeighty or three hundred sixty degrees can be received. When the inputangular view value is reached according to the angular views determinedfrom the IMU data, the recording can stop. In another embodiment, a usermay manually stop the image selection, such as by providing a touchinput or a voice command. In yet another embodiment, the user may simplytilt the camera so that the object is no longer in the camera view andthe MVIDMR system may end the image selection process.

In 1506, an angle profile can be received. The angle profile can be usedto specify angles at which to select images from among the live images.For example, an angle increment, such as one degree, two degrees, fivedegrees, ten degrees, etc., can be specified in the angle profile. Inanother example, a list of angle values for selecting images can bespecified, such as five degrees, seven degrees, nine degrees, fourteendegrees, nineteen degrees, twenty nine degrees, thirty nine degrees,etc., where the spacing between the angle values can be varied. Thesevalues can be entered manually and stored in a file. Then, the valuescan be reread from the file if reused for different objects, such as twodifferent shoes or two different cars.

In 1508, initial IMU data and live image data can be received. In oneembodiment, the initial angle value can start at zero and then adetermination of angular changes from the IMU data can start. Thus, oneof the live images initially received can be selected for an imagesequence used to generate an MVIDMR.

In addition, when the image selection process ends one of the last liveimages received can be also be saved. The last image saved may not be inaccordance with a specified angular spacing. For example, when a fivedegree angular increment is specified and the image selection processends with less than a five degree increment between the last imageselected, then an image with less than a five degree spacing can beselected. In one embodiment, the selected images in the image sequencecan be trimmed such that it ends with an equal spacing. For example, ifthe angular spacing is five degrees and the image selection process endswith only a three degree spacing between the last available image andthe last selected image, then the image sequence can be trimmed down tothe last selected image.

In 1510, based upon IMU data, an orientation of a device including acamera can be determined. For example, a pitch and roll orientation ofthe device relative to the gravity vector can be determined. Theorientation changes can be used to estimate angular changes. In anotherembodiment, an object appearing in the live images can be selected, thenangular rate measurements from the IMU can be integrated to determineangular changes to an orientation of the device. The angular changes canbe used to estimate an angular view of the selected object which hasbeen captured.

In 1512, based upon an orientation of the device (alternatively, basedupon angular rates), an angular change can be determined from previousorientation of the device. The angular changes can be summed todetermine a current angular view of the object that has been capturedsince the MVIDMR process was initiated. As described above, the currentangular view can be associated with an image received from the camerathat has been selected and stored.

In 1514, a current angle in the angle profile can be determined. Forexample, if five degree increments are being used, the first angle inthe angle profile can be five degrees and the second angle in the angleprofile can be ten degrees. After an image is selected that isassociated with the first angle, the MVIDMR system can advance to thesecond angle. This process can continue until images are selected thatare associated with all the angles in the angle profile or the MVIDMRimage selection process ends, such as when a desired angular view valueis reached.

In 1516, a current captured angular view determined from the IMU datacan be compared to the current angle in the angle profile. In oneembodiment, when the current captured angular view is greater thancurrent angle in the angle profile than a current live image can beselected. The selected live image and its angular view value can bestored in 1518. When the current live image is selected, a new currentangle can be determined. In another embodiment, when the current angularview is within a range of the current angle in the angle profile, than acurrent live image can be selected. For example, the range can be plusor minus a quarter of degree, a half a degree, a degree, etc.

As described above, the live images can be associated with an angularview value. As described above, negative angular changes can occur andcan be ignored. Thus, in one embodiment, only live images associatedwith a positive change in the angular view value can be selected andstored. The positive change can refer to rotation in a desired directionalong a path. The live images associated with a negative angular viewchange can be ignored. Thus, a selection of live images based uponpositive change in the angular view value can be one filter forselecting from among the live images. In this example, the angularspacing between the selected live images can vary from image to imagedepending on how the mobile device including the camera is moved fromimage to image.

In 1516, when the current captured angular view is not greater than thecurrent angle in the angle profile, the process flow can advance to1520. Further, the process flow can advance to 1520 after an image isselected and stored in 1518. In 1520, the current angular view value canbe compared to the angular view value received in 1504. When the currentangular view value is not greater than the angular view value or withina range, then the process flow can return to 1508.

When the current angular view value is greater than the angular viewvalue or within a range of the angular view value, then a final imagecan be selected and stored to the image sequence used to generate theMVIDMR. Alternately, when the MVIDMR ends, such as via a manual command,then a final image can be selected and stored. Then, the image selectionprocess can end.

As described above, when the image selection process ends, a trimmingprocess can occur. For example, when a constant angular spacing has beenspecified and the last image saved is less than the constant angularspacing then the last image saved can be removed from the image sequenceused for the MVIDMR generation. In another example, when a three hundredsixty degree angular view is generated and the last image or imagesselected are at an angular view greater than three hundred sixtydegrees, then some of the last images selected can be trimmed from theimage sequence or some of the first images selected can be trimmed fromthe image sequence such that the overlap is removed. In yet anotherembodiment, the overlapping images can be merged with one another. Forexamples, selected images with an angular view value greater than threehundred sixty degrees can be merged with selected images at thebeginning of the image sequence to close the loop.

With reference to FIG. 16, shown is a particular example of a computersystem that can be used to implement particular examples of the presentinvention. For instance, the computer system 2300 can be used to providemulti-view interactive digital media representations according tovarious embodiments described above. According to particular exampleembodiments, a system 2300 suitable for implementing particularembodiments of the present invention includes a processor 2301, a memory2303, an interface 2311, and a bus 2315 (e.g., a PCI bus).

The system 2300 can include one or more sensors, such as light sensors,accelerometers, gyroscopes, multi-axis magnetometers, microphones,cameras including stereoscopic capabilities or structured light cameras.As described above, the accelerometers and gyroscopes may beincorporated in an IMU. The sensors can be used to detect movement of adevice and determine a position of the device. Further, the sensors canbe used to provide inputs into the system. For example, a microphone canbe used to detect a sound or input a voice command.

In the instance of the sensors including one or more cameras, the camerasystem can be configured to output native video data as a live videofeed. The live video feed can be augmented and then output to a display,such as a display on a mobile device. The native video can include aseries of frames as a function of time. The frame rate is oftendescribed as frames per second (fps). Each video frame can be an arrayof pixels with color or gray scale values for each pixel. For example, apixel array size can be 512 by 512 pixels with three color values (red,green and blue) per pixel. The three color values can be represented byvarying amounts of bits, such as 24, 30, 36, 40 bits, etc. per pixel.When more bits are assigned to representing the RGB color values foreach pixel, a larger number of colors values are possible. However, thedata associated with each image also increases. The number of possiblecolors can be referred to as the color depth.

The video frames in the live video feed can be communicated to an imageprocessing system that includes hardware and software components. Theimage processing system can include non-persistent memory, such asrandom access memory (RAM) and video RAM (VRAM). In addition,processors, such as central processing units (CPUs) and graphicalprocessing units (GPUs) for operating on video data and communicationbusses and interfaces for transporting video data can be provided.Further, hardware and/or software for performing transformations on thevideo data in a live video feed can be provided.

In particular embodiments, the video transformation components caninclude specialized hardware elements configured to perform functionsnecessary to generate a synthetic image derived from the native videodata and then augmented with virtual data. In data encryption,specialized hardware elements can be used to perform a specific datatransformation, i.e., data encryption associated with a specificalgorithm. In a similar manner, specialized hardware elements can beprovided to perform all or a portion of a specific video datatransformation. These video transformation components can be separatefrom the GPU(s), which are specialized hardware elements configured toperform graphical operations. All or a portion of the specifictransformation on a video frame can also be performed using softwareexecuted by the CPU.

The processing system can be configured to receive a video frame withfirst RGB values at each pixel location and apply operation to determinesecond RGB values at each pixel location. The second RGB values can beassociated with a transformed video frame which includes synthetic data.After the synthetic image is generated, the native video frame and/orthe synthetic image can be sent to a persistent memory, such as a flashmemory or a hard drive, for storage. In addition, the synthetic imageand/or native video data can be sent to a frame buffer for output on adisplay or displays associated with an output interface. For example,the display can be the display on a mobile device or a view finder on acamera.

In general, the video transformations used to generate synthetic imagescan be applied to the native video data at its native resolution or at adifferent resolution. For example, the native video data can be a 512 by512 array with RGB values represented by 24 bits and at frame rate of 24fps. In one embodiment, the video transformation can involve operatingon the video data in its native resolution and outputting thetransformed video data at the native frame rate at its nativeresolution.

In other embodiments, to speed up the process, the video transformationsmay involve operating on video data and outputting transformed videodata at resolutions, color depths and/or frame rates different than thenative resolutions. For example, the native video data can be at a firstvideo frame rate, such as 24 fps. But, the video transformations can beperformed on every other frame and synthetic images can be output at aframe rate of 12 fps. Alternatively, the transformed video data can beinterpolated from the 12 fps rate to 24 fps rate by interpolatingbetween two of the transformed video frames.

In another example, prior to performing the video transformations, theresolution of the native video data can be reduced. For example, whenthe native resolution is 512 by 512 pixels, it can be interpolated to a256 by 256 pixel array using a method such as pixel averaging and thenthe transformation can be applied to the 256 by 256 array. Thetransformed video data can output and/or stored at the lower 256 by 256resolution. Alternatively, the transformed video data, such as with a256 by 256 resolution, can be interpolated to a higher resolution, suchas its native resolution of 512 by 512, prior to output to the displayand/or storage. The coarsening of the native video data prior toapplying the video transformation can be used alone or in conjunctionwith a coarser frame rate.

As mentioned above, the native video data can also have a color depth.The color depth can also be coarsened prior to applying thetransformations to the video data. For example, the color depth might bereduced from 40 bits to 24 bits prior to applying the transformation.

As described above, native video data from a live video can be augmentedwith virtual data to create synthetic images and then output inreal-time. In particular embodiments, real-time can be associated with acertain amount of latency, i.e., the time between when the native videodata is captured and the time when the synthetic images includingportions of the native video data and virtual data are output. Inparticular, the latency can be less than 100 milliseconds. In otherembodiments, the latency can be less than 50 milliseconds. In otherembodiments, the latency can be less than 30 milliseconds. In yet otherembodiments, the latency can be less than 20 milliseconds. In yet otherembodiments, the latency can be less than 10 milliseconds.

As described above, tracking an object can refer to tracking one or morepoints from frame to frame in the 2-D image space. The one or morepoints can be associated with a region in the image. The one or morepoints or regions can be associated with an object. However, the objectdoesn't have to be identified in the image. For example, the boundariesof the object in 2-D image space don't have to be known. Further, thetype of object doesn't have to be identified. For example, adetermination doesn't have to be made as to whether the object is a car,a person or something else appearing in the pixel data.

One advantage of tracking objects in the manner described above in the2-D image space is that a 3-D reconstruction of an object or objectsappearing in an image don't have to be performed. The 3-D reconstructionstep can be referred to as “structure from motion (SFM)” in the computervision community and “simultaneous localization and mapping (SLAM)” inthe robotics community. The 3-D reconstruction can involve measuringpoints in multiple images, and the optimizing for the camera poses andthe point locations. When this process is avoided, significantcomputation time is saved. For example, avoiding the SLAM/SFMcomputations can enable the methods to be applied when objects in theimages are moving. Typically, SLAM/SFM computations assume staticenvironments.

The interface 2311 may include separate input and output interfaces, ormay be a unified interface supporting both operations. Examples of inputand output interfaces can include displays, audio devices, cameras,touch screens, buttons and microphones. When acting under the control ofappropriate software or firmware, the processor 2301 is responsible forsuch tasks such as optimization. Various specially configured devicescan also be used in place of a processor 2301 or in addition toprocessor 2301, such as graphical processor units (GPUs). The completeimplementation can also be done in custom hardware. The interface 2311is typically configured to send and receive data packets or datasegments over a network via one or more communication interfaces, suchas wireless or wired communication interfaces. Particular examples ofinterfaces the device supports include Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces, andthe like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as packet switching, media control and management.

According to particular example embodiments, the system 2300 uses memory2303 to store data and program instructions and maintained a local sidecache. The program instructions may control the operation of anoperating system and/or one or more applications, for example. Thememory or memories may also be configured to store received metadata andbatch requested metadata.

In FIG. 16, the system 2300 can be integrated into a single device witha common housing. For example, system 2300 can include a camera system,processing system, frame buffer, persistent memory, output interface,input interface and communication interface. In various embodiments, thesingle device can be a mobile device like a smart phone, an augmentedreality and wearable device like Google Glass™ or a virtual reality headset that includes a multiple cameras, like a Microsoft Hololens™. Inother embodiments, the system 2300 can be partially integrated. Forexample, the camera system can be a remote camera system. As anotherexample, the display can be separate from the rest of the componentslike on a desktop PC.

In the case of a wearable system, like a head-mounted display, asdescribed above, a virtual guide can be provided to help a user record amulti-view interactive digital media representation. In addition, avirtual guide can be provided to help teach a user how to view amulti-view interactive digital media representation in the wearablesystem. For example, the virtual guide can be provided in syntheticimages output to head mounted display which indicate that the multi-viewinteractive digital media representation can be viewed from differentangles in response to the user moving some manner in physical space,such as walking around the projected image. As another example, thevirtual guide can be used to indicate a head motion of the user canallow for different viewing functions. In yet another example, a virtualguide might indicate a path that a hand could travel in front of thedisplay to instantiate different viewing functions.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present inventionrelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include hard disks,floppy disks, magnetic tape, optical media such as CD-ROM disks andDVDs; magneto-optical media such as optical disks, and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory devices (ROM) and programmable read-only memorydevices (PROMs). Examples of program instructions include both machinecode, such as produced by a compiler, and files containing higher levelcode that may be executed by the computer using an interpreter.

Although many of the components and processes are described above in thesingular for convenience, it will be appreciated by one of skill in theart that multiple components and repeated processes can also be used topractice the techniques of the present disclosure.

While the present disclosure has been particularly shown and describedwith reference to specific embodiments thereof, it will be understood bythose skilled in the art that changes in the form and details of thedisclosed embodiments may be made without departing from the spirit orscope of the invention. It is therefore intended that the invention beinterpreted to include all variations and equivalents that fall withinthe true spirit and scope of the present invention.

What is claimed is:
 1. A method comprising: on a mobile device includinga processor, a memory, a camera, an inertial measurement unit, amicrophone and a touchscreen display, receiving via an input interfaceon the mobile device a request to generate a multi-view interactivedigital media representation of an object; causing capture of liveimages from the camera on the mobile device as the mobile device movesalong a path and wherein an orientation of the camera varies along thepath such that the object in the live images is captured from aplurality of camera views; based upon sensor data from the inertialmeasurement unit, determining angular changes in the orientation of thecamera along the path; based upon the angular changes, determining anangular view of the object captured in each of the live images; basedupon the determined angular view of the object in each of the liveimages, selecting a sequence of images from among the live images;determining the angular view of the object is about three hundred sixtydegrees in one of the live images; selecting a final image in thesequence of images wherein the angular view of the object in the finalimage is about three hundred sixty degrees; automatically stopping,responsive to selecting the final image in the sequence of images,capture of the live images; and generating from the sequence of theimages the multi-view interactive digital media representation whereinthe multi-view interactive digital media representation includes aplurality of images wherein each of the plurality of images includes theobject from a different camera view such that when the plurality ofimages is output to the touchscreen display the object appears toundergo a 3-D rotation through about three hundred sixty degrees viewwherein the three hundred sixty degree 3-D rotation of the object isgenerated without a 3-D polygon model of the object.
 2. The method ofclaim 1, wherein the object is a car.
 3. The method of claim 2, furthercomprising causing display of, on a display device of a user, themulti-view interactive digital media representation of the car, themulti-view interactive digital media representation of the car beingdisplayed as a 360 degree tour of the car, the 360 degree tour of thecar being a component of an advertisement of the car.
 4. The method ofclaim 3, further comprising changing, based on input from the user, themulti-view interactive digital media representation of the car, asdisplayed on the device of the user, such that the multi-viewinteractive digital media representation of the car is displayed from adifferent viewpoint.
 5. The method of claim 2, further comprisingremoving the final image to provide a smoother transition when themulti-view interactive digital media representation is cycled from anend to its beginning or from the beginning to its end.
 6. The method ofclaim 2, wherein one or more of a height of the camera above ground, adistance from the camera to the object, a pitch angle of the mobiledevice, a roll angle of the mobile device, a yaw angle of the mobiledevice or combinations thereof varies along the path.
 7. The method ofclaim 2, further comprising determining a pitch angle and roll angle ofthe mobile device and based upon the pitch angle and the roll angledetermining the angular view of the object is about three hundred sixtydegrees.
 8. The method of claim 2, further comprising, in response toreceiving the request generate the multi-view interactive digital mediarepresentation, selecting a first image in the sequence of images fromamong the live images wherein the angular view of the object associatedwith the first image is zero.
 9. The method of claim 2, furthercomprising receiving via the input interface an angle profile whereinthe angle profile includes a plurality of angles or angle informationused to determine the plurality of angles wherein the plurality ofangles in the angle profile are each spaced equally.
 10. The method ofclaim 9, wherein the angle information indicates a constant anglespacing value to use between each of the plurality of angles.
 11. Themethod of claim 9, further comprising determining a final image selectedfor the sequence of images is not equally spaced with a preceding imagein the sequence of images and removing the final image from the sequenceof images prior to generating the multi-view interactive digital mediarepresentation.
 12. The method of claim 1, further comprisingdetermining whether each of the angular changes is a positive inmagnitude or negative in magnitude and selecting only live imagesassociated with a positive angular change.
 13. The method of claim 1,further comprising receiving via the input interface an angle profilewherein the angle profile includes a plurality of angles or angleinformation used to determine the plurality of angles wherein an angularspacing between each of the plurality of angles is variable.
 14. Themethod of claim 1, further comprising receiving via the input interfacean angle profile wherein the angle profile includes a plurality ofangles or angle information used to determine the plurality of angleswherein a portion of the plurality of angles is clustered around aparticular angular view of the object such that a first angular spacingnear the particular angular view of the object is smaller than a secondangular spacing away from the particular angular view.
 15. The method ofclaim 1, wherein the angular changes are determined about an axis. 16.The method of claim 15, wherein the axis is aligned with Earth's gravityvector.
 17. The method of claim 15, further comprising based upon theorientation of the camera along the path, determining a first directionof rotation of the camera about the axis.
 18. The method of claim 17,wherein only angular changes in the first direction are used todetermine the angular view of the object captured in each of the liveimages.
 19. The method of claim 18, further comprising determining afirst angular change in a second direction opposite the first direction,determining a subsequent second angular change in the first direction,wherein the second angular change is determined based upon theorientation of the camera prior to the first angular change.
 20. Themethod of claim 17, further comprising determining angular changes inthe first direction of rotation and in a second direction opposite thefirst direction wherein the angular view of the objected captured ineach of the live images is determined using both the angular changes inthe first direction and the second direction.